Exploring the Role of Technology in the Entire Process of the COVID-19 Epidemic Emergency Management in a Local Government A

Exploring the Role of Technology in the Entire Process of the COVID-19 Epidemic Emergency Management in a Local Government: A Case of Hainan Province


It is impossible to exaggerate the importance of big data technology in the four phases of the Hainan government’s COVID-19 pandemic emergency management plan, which are described below. As COVID-19 spreads worldwide, traditional disaster management strategies in the four COVID-19 emergency management phases (mitigation, preparedness, response, and recovery) are encountering considerable practical difficulties, according to the organization. With the use of large-scale data analytics technologies, local governments may now combat the COVID-19 outbreak in a more scientific manner than was before feasible.

In order to use big data technology in the Mitigation stage of the COVID-19 pandemic emergency management, legislators and government agencies need adopt laws, rules, techniques, policies, organizational structures, and operational processes. In order to use big data technology in pandemic emergency management, the legislative branch should develop rules and regulations that establish the objectives, methods of use, scopes, limits, themes, duties, rights, and responsibility associated with the technology. The legislative branch should also develop laws and regulations that outline the advantages of big data technology in pandemic emergency management. Government agencies should establish or designate appropriate agencies in accordance with applicable law in order to increase accountability for the use of big data technology in pandemic emergency management and to establish a clear mechanism for cross- and multi-agency collaboration in order to combat pandemic spread. The COVID-19 epidemic mitigation stage is also concerned with the construction of a well-coordinated and efficient epidemic command structure. The National COVID-19 Pandemic Control and Prevention Command of China, an official agency established at all levels of the Chinese government in response to these developments, has been tasked with overseeing and coordinating the use of big data technology for COVID-19 epidemic emergency management by key agencies throughout the country. The availability of Hainan’s big data management agencies at the time facilitated the application of big data technologies in the COVID-19 pandemic emergency management response efforts. They were able to do this by creating the legal and organizational basis for the use of big data technologies to the COVID-19 epidemic tragedy. The use of big data technologies may also allow authorities to predict the possibility of a local outbreak, the length and intensity of the COVID-19 epidemic, model the transmission mechanism, and assist in correctly budgeting for the epidemic’s emergency management budget. It is also feasible to establish a comprehensive and full case database for epidemic emergency management with the use of big data technologies, which would make the process of creating COVID-19 epidemic emergency plans in less time much easier.

Several government agencies and non-governmental organizations should collaborate to develop a big data platform, an Internet of Things platform, and a public health information system as part of the COVID-19 19 epidemic emergency management preparedness stage. These platforms will serve as the digital foundation for managing an early warning system, epidemic information, and emergencies, among other functions. For epidemic emergency management, the application of big data technologies to increase the prediction and early warning capacities of a public health monitoring system is critical. Aside from that, the use of Internet of Things technology to manage emergency resources (such as disease control specialists and other medical personnel, as well as supplies and food) expedites the deployment of epidemic emergency management. A national and provincial epidemic surveillance system, enabled by big data technology’s interoperability and shareability, can aid in the development of an integrated coordination mechanism connecting central and local governments, as well as all government departments. This will be particularly useful during the COVID-19 epidemic emergency management, when the administrative preparedness for epidemic emergency management will be improved. Big data technology is being used to collect and analyze real-world data on medical care, transportation, communication, consumption, positioning, and social media. The goal is to reduce or even eliminate digital discrimination against marginalized groups while increasing citizen engagement and ensuring adequate political preparedness for emergency response. Local governments may be able to employ big data technologies to make more accurate judgments on disaster drills, emergency force mobilization, and emergency resource management. Furthermore, big data technology enables local governments to assess the risk of the COVID-19 epidemic spreading, as well as the pace and size of transmission, via the use of statistical models.

Big data technologies are being utilized to keep track of the pandemic scenario during the COVID-19 epidemic emergency management response stage, which is now underway. It makes it simpler to do damage assessments, issue alerts in the event of an emergency, monitor dynamic conditions, perform emergency rescues, and comply with legislative processes. The Internet of Things (IoT) and big data technologies such as cloud computing aid in the efficient deployment of emergency resources, which helps to enhance COVID-19 reaction times and effectiveness. When combined with active conventional emergency management methods such as contact tracing, quarantine, treatment, lockdown, and isolation during a pandemic, the use of big data to gain situational awareness of the COVID-19 pandemic results in a significant improvement in the responsiveness and accuracy of epidemic emergency management during an outbreak of the COVID-19 virus. For pandemic warning, monitoring, and data-driven decision-making, a digital emergency response system that links national government levels, government agencies, and governments in general to society is required.

Big data is being used to aid in the recovery stage of the COVID-19 pandemic emergency management, among other things, to help with post-epidemic reconstruction, employment resumption, and school reopenings among other tasks. Emergency response skills, urban resilience, and the ability to react rapidly in the event of a public health crisis may all be improved as a consequence of this. Large-scale data collection and analysis were critical in China’s post-epidemic phase in terms of giving economic support to needy patients and delivering consumer subsidies to the general public. Federal, state, and local governments should pool their resources and expertise to assist in the development of legislation, platforms, and technology for the use of big data in pandemic emergency management.Q2. Discuss the potential challenges of social compliance and security issues (e.g., improper use, information leakage, privacy protection, transparency or accountability, etc.) when applying big data technology in COVID-19 epidemic emergency management.


When adopting big data technology to manage the COVID-19 epidemic, there are a number of possible hurdles to address, including social compliance and security concerns. Inappropriate use, information leakage, privacy protection, openness, and accountability are just a few of the issues to consider. False data production is one of the most serious security challenges confronting big data today, and it’s also one of the most difficult to address effectively. Fake data is a major source of worry in the context of big data security since it makes it more difficult to uncover other problems with the data. As an example, false flags generated by fraudulent data may make it more difficult to detect fraud. False reports may also result in the deployment of unintended tasks, which may have a negative impact on production or other critical parts of operating a company.

When it comes to big data security issues, the vast majority of customers are concerned about the protection of their personal information, according to research. For the purpose of assuaging their customers’ fears, numerous companies have turned to data masking tactics. This may provide the impression of safety, which is not desired in any situation. Data masking is a strategy used to keep vital consumer information apart from personally identifiable information about the customer. When done properly, the approach cannot be reversed; nevertheless, malicious actors that have access to the requisite technology may be able to reassemble databases from time to time. It is possible that they will be able to reconnect people with their personal data as a result of this. Aside from placing customer data at risk, it is possible that sensitive business information will be compromised as well.

Keep in mind that significant data security concerns are not necessarily associated with data breaches, as shown by the use of automated cleaners in certain cases. You may pick from a variety of manual and automatic data cleaning procedures to meet the specific needs of your firm. Although connected data properties may be inconsistent, this is not always the case if an automated data cleaning approach is based on a faulty model, as in the case of the example above. The second major difficulty with automated cleaners is that data management workers may get used to utilizing them as a result of their widespread use in the industry. The general quality of your database may be reduced as a result of this, but the likelihood of data breaches is increased as well. According to the facts, security complacency is one of the most common reasons for data breaches in countries all over the globe.

Another of the most significant challenges associated with big data is the vast variety of data sets that may be employed at any one moment. These snippets of information may be organized or unstructured, and they can originate from a number of sources, including mobile devices, servers, email files, cloud apps, and other types of data storage devices. As data grows more complex, it becomes more difficult to protect it, making it imperative to implement a trustworthy extract, transform, and load (ETL) service in order to increase data compatibility. Data mining is a strong strategy that may be able to assist you in better understanding and using the information your firm has acquired. If you use your database systems in conjunction with statistical and machine learning methodologies, you may be able to uncover trends that will allow you to better target your customers’ needs. The fact that this procedure is controlled by specialists does not diminish the fact that it has the potential to generate major data security concerns that must not be neglected.

Big data storage, as part of the total system, should ideally incorporate real-time capabilities for ensuring security compliance and compliance with regulations. Processes are regularly monitored by these resources to verify that a corporation is taking the required precautions to protect sensitive information. The problem is that when these technologies are implemented, they generate an enormous volume of data that must be filtered and analyzed. It is possible that hackers will gain access to your other databases as a result of a data breach in one of your databases. These real-time compliance solutions are critical, despite the fact that they generate a significant amount of new data that must be protected from unauthorized access. Before making a selection, be certain that you have properly researched all of your possibilities. Look for a system that contains technologies that reduce the likelihood of false positives being generated by the system. It is possible that a single erroneous breach alert may result in the waste of resources while also enabling real breaches to go undetected.


Effective emergency management is critical for minimizing public health hazards associated with an infectious disease outbreak that has begun to spread. Controlling the COVID-19 epidemic has become a huge worldwide challenge, as well as a significant one in a number of locations. Computers and other information technology (IT) systems may be used to monitor surveillance systems and artificial intelligence. Depending on the technology used, they could also be used during the response phase of an outbreak management application in Singapore. Information technology has been utilized to increase diagnostic accuracy, early detection, healthcare worker safety, task reduction, time and cost savings, and the development of novel medications. Without the use of information technology (IT), it may be difficult to handle and manage a large-scale catastrophe. To both mitigate and mitigate risk, we must use information technology (IT) in crisis circumstances. Communities may use information and communications technology (ICT) capabilities throughout the preventative, prepared, and recovery phases, as well as during the response phase. COVID-19’s recovery is anticipated to be aided significantly by information technology. When governments attempt to develop IT skills, they should pay more attention to the way they supply information technology infrastructure and the amount of money they spend.

Governments have collaborated to limit and ameliorate the consequences of climate change on a global scale, with various degrees of success. Countries with low COVID-19 per capita mortality seem to have in place procedures that include early surveillance, testing, contact tracing, and tight quarantine restrictions. The majority of nations that have performed well have used digital technology in policy and health care to provide the necessary degree of coordination and data management for these initiatives to succeed. This Viewpoint places a premium on the use of digital technology to manage and react to pandemics. It demonstrates how well they have been utilized by governments for pandemic planning, monitoring, and testing, contact tracing and quarantine, and health care delivery, among other things.

Massive quantities of data and artificial intelligence (AI) have aided people in better preparing for COVID-19 and tracking down individuals, hence reducing disease transmission in a variety of locations. Chinese officials were able to follow the travels of persons who visited the Wuhan market, which was the epicenter of the epidemic, using mobile mapping applications such as migration maps. These systems gather real-time location data from people’s mobile phones, mobile payment applications, and social networking applications and display it on maps. They might even be employed in Singapore if necessary. Machine learning models were created using this data to forecast COVID-19 transmission patterns in various regions of the globe and to aid in border inspections and surveillance.

In the Chinese province of Hainan, a free web- and cloud-based system was utilized to filter and lead visitors to the appropriate material. Thermal pictures of persons are captured in real time by very high-quality infrared thermal cameras used at airports. This enables the rapid identification of those suffering from a fever. In Singapore, technology may be used to take people’s temperatures at the entrances of companies, schools, and public transit, preventing them from being ill. The thermometer data is recorded. It may be used to identify novel disease hotspots and clusters that can subsequently be tested for.

In Singapore, surveillance camera video, face recognition, credit card records, and GPS data from automobiles and phones may be utilized to get real-time information on people’s movements and precise travel schedules. This technique is referred to as “aggressive contact tracing.” Singaporeans may get SMS notifications if a new case of COVID-19 is discovered in their neighborhood. Individuals who have been in touch with COVID-19 carriers are urged to visit testing centers and avoid interaction with other persons. Singapore must identify and eradicate illnesses as soon as they begin in order to maintain one of the lowest mortality rates per person in the world.


SRM helps people, businesses, and communities better understand security threats and how they interact. The following are prominent systems engineering methodologies for reducing or mitigating recognized program risks. Collaborate with operational users to understand risks and their effects. Risks may be classified as having an impact on cost, schedule, or performance. An impact on the mission’s ability to achieve its goals should be considered as a risk. Learn all you can about their ramifications. When determining the “assume/accept” option to utilize in the final decision making process, including users is critical. Users’ willingness to accept risk’s repercussions will affect risk acceptance. Help users comprehend the risks, the countermeasures, and the residual risk. Help users comprehend time and money spent.

Again, work with users to build a common understanding of risks and dangers. Predict the schedule changes needed to reduce risk associated with technical maturity or additional development to improve user performance. Identify the delayed capabilities and any implications that may occur from reliance on other activities. This information will help users understand the operational repercussions of a “avoid” option.

You may help risk management by researching alternative risk reduction strategies. Using a commercially accessible capability rather than a contractor-developed capability is one option. Look for solutions from other MITRE clients, industry, and academics that have faced similar risk challenges in the past. When choosing a third-party solution, carefully consider the implications of any architectural changes that may be necessary.

Changing the organization responsible for a risk area might be useful or bad. This strategy may be appropriate when the risk requires specialized expertise not often found in program offices. Moving a risk to another organization, on the other hand, may lead to new difficulties such as dependencies and control loss. You and your client might investigate a transfer possibility by learning about businesses within your client’s industry that specialize on particular needs and solutions. It is preferable to be aware of this early in the program acquisition cycle, when options for transfer are more readily accessible.